{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b84009f7-cb6c-4f6e-b8e4-6168eb3e2afd",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'load_btrast_cancer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m x,y\u001b[38;5;241m=\u001b[39mload_btrast_cancer()\u001b[38;5;241m.\u001b[39mdata,load_breast_cancer()\u001b[38;5;241m.\u001b[39mtarget\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'load_btrast_cancer' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "x, y = load_breast_cancer().data, load_breast_cancer().target\n",
    "print(x.shape)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0574b517-dda9-454c-b505-193947cf67e3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n\u001b[1;32m----> 2\u001b[0m x \u001b[38;5;241m=\u001b[39m StandardScaler()\u001b[38;5;241m.\u001b[39mfit_transform(x)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "x = StandardScaler().fit_transform(x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4fb1181-18d0-4ae4-b1ba-821da305048a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
